This paper presents a new way to describe the Arctic sea-ice changes based on the shape of the observed seasonal cycles and using machine learning techniques. We show that the East Siberian and Laptev seas have lost their typical permanent sea-ice seasonal cycle while the Kara and Chukchi seas are experiencing a new typical seasonal cycle, corresponding to a partial winter-freezing.

Old observations are necessary to understand the atmosphere. When direct observations are not available, one can use indirect observations, such as tide gauges, which measure the sea level in port cities. The sea level rises when local air pressure decreases and when wind pushes water towards the coast. Several centuries-long tide gauge records are available. We show that these can be complementary to direct pressure observations for studying storms and anticyclones in the 19th century.

This paper explores potential technical enhancements to wind lidar profilers for improved turbulence measurement. The study separately tests the effects of increased sampling rate and reduced probe length against a commercial lidar of the same type. Various turbulence metrics were quantified to evaluate the impact of these technical modifications. The results indicate that increasing the sampling rate is the most valuable enhancement for turbulence measurement.

The goal of this paper is to weight several dynamic models in order to improve the representativeness of a system. It is illustrated using a set of versions of an idealized model describing the Atlantic Meridional Overturning Circulation. The low-cost method is based on data-driven forecasts. It enables model performance to be evaluated on their dynamics. Taking into account both model performance and codependency, the derived weights outperform benchmarks in reconstructing a model distribution.

This study examines motion's impact on LOS turbulent velocity fluctuations measured by lidar profilers. Onshore tests used a mobile lidar (WindCube v2.1) on a hexapod, comparing it to a fixed lidar. RMSE was calculated to assess motion effects on turbulence. Results showed alignment, wind speed and amplitude as significant influences on RMSE. Motion frequency affected LOS velocity spectra but had limited impact on RMSE compared to other factors.